|
|
import torch
|
|
|
import torch.nn.functional as F
|
|
|
import torch.nn as nn
|
|
|
from torch.nn import Conv1d, AvgPool1d, Conv2d
|
|
|
from torch.nn.utils import weight_norm, spectral_norm
|
|
|
|
|
|
from .utils import get_padding
|
|
|
|
|
|
LRELU_SLOPE = 0.1
|
|
|
|
|
|
def stft(x, fft_size, hop_size, win_length, window):
|
|
|
"""Perform STFT and convert to magnitude spectrogram.
|
|
|
Args:
|
|
|
x (Tensor): Input signal tensor (B, T).
|
|
|
fft_size (int): FFT size.
|
|
|
hop_size (int): Hop size.
|
|
|
win_length (int): Window length.
|
|
|
window (str): Window function type.
|
|
|
Returns:
|
|
|
Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
|
|
|
"""
|
|
|
x_stft = torch.stft(x, fft_size, hop_size, win_length, window,
|
|
|
return_complex=True)
|
|
|
real = x_stft[..., 0]
|
|
|
imag = x_stft[..., 1]
|
|
|
|
|
|
return torch.abs(x_stft).transpose(2, 1)
|
|
|
|
|
|
class SpecDiscriminator(nn.Module):
|
|
|
"""docstring for Discriminator."""
|
|
|
|
|
|
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False):
|
|
|
super(SpecDiscriminator, self).__init__()
|
|
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
|
|
self.fft_size = fft_size
|
|
|
self.shift_size = shift_size
|
|
|
self.win_length = win_length
|
|
|
self.window = getattr(torch, window)(win_length)
|
|
|
self.discriminators = nn.ModuleList([
|
|
|
norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
|
|
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
|
|
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
|
|
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
|
|
|
norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))),
|
|
|
])
|
|
|
|
|
|
self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))
|
|
|
|
|
|
def forward(self, y):
|
|
|
|
|
|
fmap = []
|
|
|
y = y.squeeze(1)
|
|
|
y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device()))
|
|
|
y = y.unsqueeze(1)
|
|
|
for i, d in enumerate(self.discriminators):
|
|
|
y = d(y)
|
|
|
y = F.leaky_relu(y, LRELU_SLOPE)
|
|
|
fmap.append(y)
|
|
|
|
|
|
y = self.out(y)
|
|
|
fmap.append(y)
|
|
|
|
|
|
return torch.flatten(y, 1, -1), fmap
|
|
|
|
|
|
class MultiResSpecDiscriminator(torch.nn.Module):
|
|
|
|
|
|
def __init__(self,
|
|
|
fft_sizes=[1024, 2048, 512],
|
|
|
hop_sizes=[120, 240, 50],
|
|
|
win_lengths=[600, 1200, 240],
|
|
|
window="hann_window"):
|
|
|
|
|
|
super(MultiResSpecDiscriminator, self).__init__()
|
|
|
self.discriminators = nn.ModuleList([
|
|
|
SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
|
|
|
SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
|
|
|
SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)
|
|
|
])
|
|
|
|
|
|
def forward(self, y, y_hat):
|
|
|
y_d_rs = []
|
|
|
y_d_gs = []
|
|
|
fmap_rs = []
|
|
|
fmap_gs = []
|
|
|
for i, d in enumerate(self.discriminators):
|
|
|
y_d_r, fmap_r = d(y)
|
|
|
y_d_g, fmap_g = d(y_hat)
|
|
|
y_d_rs.append(y_d_r)
|
|
|
fmap_rs.append(fmap_r)
|
|
|
y_d_gs.append(y_d_g)
|
|
|
fmap_gs.append(fmap_g)
|
|
|
|
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
|
|
|
|
|
|
|
|
class DiscriminatorP(torch.nn.Module):
|
|
|
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
|
|
super(DiscriminatorP, self).__init__()
|
|
|
self.period = period
|
|
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
|
|
self.convs = nn.ModuleList([
|
|
|
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
|
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
|
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
|
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
|
|
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
|
|
|
])
|
|
|
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
|
|
|
|
|
def forward(self, x):
|
|
|
fmap = []
|
|
|
|
|
|
|
|
|
b, c, t = x.shape
|
|
|
if t % self.period != 0:
|
|
|
n_pad = self.period - (t % self.period)
|
|
|
x = F.pad(x, (0, n_pad), "reflect")
|
|
|
t = t + n_pad
|
|
|
x = x.view(b, c, t // self.period, self.period)
|
|
|
|
|
|
for l in self.convs:
|
|
|
x = l(x)
|
|
|
x = F.leaky_relu(x, LRELU_SLOPE)
|
|
|
fmap.append(x)
|
|
|
x = self.conv_post(x)
|
|
|
fmap.append(x)
|
|
|
x = torch.flatten(x, 1, -1)
|
|
|
|
|
|
return x, fmap
|
|
|
|
|
|
|
|
|
class MultiPeriodDiscriminator(torch.nn.Module):
|
|
|
def __init__(self):
|
|
|
super(MultiPeriodDiscriminator, self).__init__()
|
|
|
self.discriminators = nn.ModuleList([
|
|
|
DiscriminatorP(2),
|
|
|
DiscriminatorP(3),
|
|
|
DiscriminatorP(5),
|
|
|
DiscriminatorP(7),
|
|
|
DiscriminatorP(11),
|
|
|
])
|
|
|
|
|
|
def forward(self, y, y_hat):
|
|
|
y_d_rs = []
|
|
|
y_d_gs = []
|
|
|
fmap_rs = []
|
|
|
fmap_gs = []
|
|
|
for i, d in enumerate(self.discriminators):
|
|
|
y_d_r, fmap_r = d(y)
|
|
|
y_d_g, fmap_g = d(y_hat)
|
|
|
y_d_rs.append(y_d_r)
|
|
|
fmap_rs.append(fmap_r)
|
|
|
y_d_gs.append(y_d_g)
|
|
|
fmap_gs.append(fmap_g)
|
|
|
|
|
|
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
|
|
|
|
|
class WavLMDiscriminator(nn.Module):
|
|
|
"""docstring for Discriminator."""
|
|
|
|
|
|
def __init__(self, slm_hidden=768,
|
|
|
slm_layers=13,
|
|
|
initial_channel=64,
|
|
|
use_spectral_norm=False):
|
|
|
super(WavLMDiscriminator, self).__init__()
|
|
|
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
|
|
self.pre = norm_f(Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0))
|
|
|
|
|
|
self.convs = nn.ModuleList([
|
|
|
norm_f(nn.Conv1d(initial_channel, initial_channel * 2, kernel_size=5, padding=2)),
|
|
|
norm_f(nn.Conv1d(initial_channel * 2, initial_channel * 4, kernel_size=5, padding=2)),
|
|
|
norm_f(nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)),
|
|
|
])
|
|
|
|
|
|
self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
|
|
|
|
|
|
def forward(self, x):
|
|
|
x = self.pre(x)
|
|
|
|
|
|
fmap = []
|
|
|
for l in self.convs:
|
|
|
x = l(x)
|
|
|
x = F.leaky_relu(x, LRELU_SLOPE)
|
|
|
fmap.append(x)
|
|
|
x = self.conv_post(x)
|
|
|
x = torch.flatten(x, 1, -1)
|
|
|
|
|
|
return x |
